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| Section | Objectives |
|---|---|
| Topic 1: Get data into Microsoft Fabric | - Data ingestion and integration
|
| Topic 2: Manage and secure Microsoft Fabric | - Governance and administration
|
| Topic 3: Real-time analytics | - Streaming and event processing
|
| Topic 4: Implement data engineering solutions | - Lakehouse architecture
|
| Topic 5: Implement analytics solutions | - Data warehousing and modeling
|
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NEW QUESTION # 170
You have a Fabric tenant that contains a complex semantic model. The model is based on a star schema and contains many tables, including a fact table named Sales. You need to create a diagram of the model. The diagram must contain only the Sales table and related tables. What should you use from Microsoft Power Bl Desktop?
Answer: D
Explanation:
To create a diagram that contains only the Sales table and related tables, you should use the Model view (C) in Microsoft Power BI Desktop. This view allows you to visualize and manage the relationships between tables within your semantic model. References = Microsoft Power BI Desktop documentation outlines the functionalities available in Model view for managing semantic models.
NEW QUESTION # 171
You have a Fabric tenant that contains a semantic model.
You need to prevent report creators from populating visuals by using implicit measures.
What are two tools that you can use to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
Answer: A,D
Explanation:
Microsoft Power BI Desktop (A) and Tabular Editor (B) are the tools you can use to prevent report creators from using implicit measures. In Power BI Desktop, you can define explicit measures which can be used in visuals. Tabular Editor allows for advanced model editing, where you can enforce the use of explicit measures.
References = Guidance on using explicit measures and preventing implicit measures in reports can be found in the Power BI and Tabular Editor official documentation.
NEW QUESTION # 172
You have a Fabric tenant that contains a warehouse named WH1. You run the following T-SQL query against WH1.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
We are analyzing the query:
SELECT e.[WWI Employee ID],
e.Employee,
e.[Preferred Name],
gdr.[WWI Employee ID] AS [Direct Report ID] ,
gdr.Employee AS [Direct Report]
FROM Dimension.Employee AS e
OUTER APPLY Dimension.GetDirectReports(e.[Employee Key]) AS gdr;
Key concepts:
APPLY operator: Used to join a table with a table-valued function.
OUTER APPLY ensures all rows from the left (Employee) are returned, even if the function returns no rows (similar to LEFT JOIN).
Table-valued function (TVF): Returns a set of rows (unlike scalar functions, which return a single value).
The function executes once per row from the left table, not once for the whole query.
Evaluating each statement:
" Dimension.GetDirectReports is a scalar T-SQL function. "
No # It must be a table-valued function, because OUTER APPLY requires a table expression.
" The Dimension.GetDirectReports function will run only once when the query runs. " No # With APPLY, the function runs for each row of Dimension.Employee.
" The output rows will include at least one row for each row in the Dimension.Employee table. " Yes # Because of OUTER APPLY, every employee row is included, even if there are no direct reports (in that case, the columns from gdr will be NULL).
Final Answer:
Scalar function # No
Runs once per query # No
At least one row per Employee # Yes
References:
APPLY operator in T-SQL
Table-valued functions
NEW QUESTION # 173
You have a Fabric tenant.
You need to create a semantic model that delivers fast report rendering, minimizes query latency, and maximizes flexibility for DAX calculations.
Which storage mode should you select?
Answer: A
Explanation:
For a Microsoft Fabric tenant requiring fast report rendering, minimal query latency, and maximum DAX flexibility, the appropriate storage mode is Direct Lake.
Direct Lake is a native Fabric storage mode that provides Import-like performance by loading data directly into memory from Delta tables in OneLake, bypassing the need for traditional data refreshes or slow DirectQuery translations.
Direct Lake combines the advantages of traditional modes while mitigating their common drawbacks:
Fast Rendering & Low Latency: It utilizes the same VertiPaq in-memory engine as Import mode, ensuring lightning-fast query execution for interactive reports.
Maximum DAX Flexibility: Unlike DirectQuery, which often limits DAX functions to what the source SQL can understand, Direct Lake supports the full breadth of DAX because it operates on data already paged into memory.
No Refresh Latency: It reads directly from OneLake (Delta Parquet files), meaning as soon as your data is updated in the Lakehouse or Warehouse, the semantic model can reflect those changes without waiting for a scheduled refresh.
Reduced Overhead: It eliminates data duplication and the "refresh tax" (CPU/memory spikes during scheduled imports) because it loads columns on-demand as they are needed for visuals.
Reference:
https://learn.microsoft.com/en-us/fabric/fundamentals/direct-lake-overview
NEW QUESTION # 174
You need to ensure that Contoso can use version control to meet the data analytics requirements and the general requirements. What should you do?
Answer: B
Explanation:
You need to ensure that Contoso can use version control to meet the data analytics requirements and the general requirements.
Requirement: "All the semantic models and reports for the Research division must use version control that s upports branching." Fabric supports Git integration (Azure Repos or GitHub) for semantic models and reports.
Storing in Data Lake Gen2 or OneDrive does not provide version control with branching.
The correct action is to integrate workspaces with a Git repository.
Modify the settings of the Research division workspaces to use an Azure Repos repository.
NEW QUESTION # 175
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